1 intro

## libraries ----

library(here)
library(data.table)
source(here("src", "stroop-rsa-pc.R"))
library(knitr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(mfutils)
library(ggseg)
library(ggsegExtra)
library(ggsegSchaefer)
library(ggsegGlasser)

## settings, params ----

opts_chunk$set(echo = TRUE, out.width = "100%")
theme_set(theme_bw(base_size = 10))

if (exists("params")) {
  ttype_subset <- params$ttype_subset
  prewh <- params$prewh
  measure <- params$measure
  roiset <- params$roiset
} else {
  prewh <- "none"
  measure <- "crcor"
  roiset <- "glasser2016_parcel"
}

theme_surface <- list(
  theme(
    axis.text = element_blank(), panel.grid = element_blank(), panel.border = element_blank(),
    axis.ticks = element_blank(), legend.position = c(0.5, 0.5), legend.title = element_text(size = 5), 
    legend.background = element_blank(), legend.text = element_text(size = 5), legend.direction = "horizontal",
    legend.key.height = unit(1/5, "cm"), legend.key.width = unit(1/4, "cm")
  )
)


## constants ----

wd <- here()
subjlists <- c("mc1", "mi1")
seswaves <- c("baseline_wave1", "proactive_wave1")
sessions <- c("baseline", "proactive")
glmname <- "lsall_1rpm"
statistics <- c("m", "t_stat")
titles <- c("mean coefficient", "t statistic")
ttype_subsets <- c("pc50", "bias")

## data ----

## read regression weights:

dat <- lapply(
  ttype_subsets,
  function(ttype_subset) {
    fname <- construct_filename_weights(
        measure = measure, subjlist = subjlists, glmname = glmname, 
        ttype_subset = ttype_subset, roiset = roiset, prewh = prewh, suffix = paste0("__seswave-", seswaves)
        )
    d <- rbindlist(lapply(fname, fread))
    d$ttype_subset <- ttype_subset
    d
  }
)
dat <- rbindlist(dat)


## get ggseg atlas and format data to match:

dat <- dat %>% rename(region = roi)

if (roiset == "schaefer2018_17_200_parcel") {
  
  atlas <- schaefer17_200
  k <- schaefer2018_17_200_fsaverage5$key %>% select(region = parcel, network)
  
  rois <- data.frame(
    region = c(
    "17Networks_LH_ContA_PFClv_1",
    "17Networks_LH_ContA_PFCl_1",
    "17Networks_LH_ContA_PFCl_2",
    "17Networks_LH_ContA_PFCl_3",
    "17Networks_RH_ContA_PFCl_1",
    "17Networks_RH_ContA_PFCl_2",
    "17Networks_LH_SalVentAttnB_PFCmp_1",
    "17Networks_RH_SalVentAttnB_PFCmp_1",
    "17Networks_LH_SalVentAttnA_FrMed_1",
    "17Networks_RH_SalVentAttnA_FrMed_1"
    ),
    location = c(rep("lateral", 6), rep("medial", 4))
  )

} else if (roiset == "glasser2016_parcel") {
  
  atlas <- glasser
  k <- glasser2016_fsaverage5$key %>% select(region = parcel, network)
  
  dat <- dat %>% filter(!region %in% "L_10pp")  ## not in glasser ggseg atlas?
  ## create region column in ggseg atlas that matches data:
  hemi <- ifelse(atlas$data$hemi == "left", "L_", "R_")
  atlas$data$region <- ifelse(is.na(atlas$data$region), NA, paste0(hemi, atlas$data$region))
  
  rois <- data.frame(
    region = combo_paste(c("L_", "R_"), c("SCEF", "8BM",  "p32pr", "a32pr", "p9-46v", "i6-8", "8Av", "8C")),
    location = c(rep("medial", 8), rep("lateral", 8))
  )
  
}

## add network information:
atlas$data <- left_join(atlas$data, k, by = "region")
dat <- left_join(dat, k, by = "region")


## calculate ----

## add subject set information:

subjs <- fread(here("out", "subjlist_mcmi.txt"))[[1]]
dat <- dat[subject %in% subjs]


dat_sum <- dat %>% 
  group_by(term, ttype_subset, session, region) %>%
  summarize(
    m = mean(b),
    t_stat = t.test(b)$statistic, 
    p = t.test(b, alternative = "greater")$p.value,
    .groups = "drop_last"
    ) %>%
  mutate(p_fdr = p.adjust(p, "fdr"))


dat_diff <- dat %>%
  pivot_wider(id_cols = c("subject", "term", "region", "ttype_subset"), names_from = "session", values_from = "b") %>%
  mutate(b = proactive - baseline) %>%
  group_by(term, ttype_subset, region) %>%
  summarize(
    m = mean(b),
    t_stat = t.test(b)$statistic,
    p = t.test(b)$p.value,
    .groups = "drop_last"
    ) %>%
  mutate(p_fdr = p.adjust(p, "fdr"), session = "pro - bas")


dat_sum <- filter(rbind(dat_sum, dat_diff), term %in% models[[measure]])
dat_sum <- dat_sum %>% group_by(term, ttype_subset, session)  ## for appropriate binding/plotting w/ ggseg


n_subjs <- length(unique(dat$subject))

This file displays group-level RSA model coefficients (means, test statistics).

Analysis parameters:

  • Item type:
  • Prewhitening type: none
  • Similarity measure: crcor
  • ROI set: glasser2016_parcel

Subject samples:

Analyses are conducted with one set of subjects:

  • the MCMIset: N = 63, unrelated, with baseline and proactive data

2 Brains

2.1 Unthresholded

2.1.1 Common scale

for (ttype_subset_i in seq_along(ttype_subsets)) {
  for (stat_i in seq_along(statistics)) {
    
    p <- dat_sum %>%
      group_by(term, ttype_subset, session) %>%
      filter(ttype_subset == ttype_subsets[ttype_subset_i]) %>%
      ggplot() +
      geom_brain(
        aes_string(fill = statistics[stat_i]), atlas = atlas, position = position_brain(side ~ hemi)
        ) +
      scale_fill_viridis_c(
        option = "magma", na.value = "grey",
        breaks = scales::extended_breaks(4)
        ) +
      facet_grid(rows = vars(term), cols = vars(session)) +
      theme_surface +
      labs(title = paste0(titles[stat_i], " ", ttype_subsets[ttype_subset_i]), fill = NULL)
    
    print(p)
  
  }
}

2.1.2 Separate scales by model

for (model_i in seq_along(models[[measure]])) {

  model <- models[[measure]][model_i]
  
  for (stat_i in seq_along(statistics)) {

    p <- dat_sum %>%
      filter(term %in% model) %>%
      ggplot() +
      geom_brain(
        aes_string(fill = statistics[stat_i]),
        atlas = atlas, position = position_brain(side ~ hemi)
        ) +
      scale_fill_viridis_c(
        option = "magma", na.value = "grey",
        breaks = scales::extended_breaks(4)
        ) +
      facet_grid(rows = vars(ttype_subset), cols = vars(session)) +
      theme_surface +
      theme(legend.position = c(2/3, 0.5)) +
      labs(title = paste0(titles[stat_i], ": ", model), fill = NULL)

    print(p)

  }

}

2.2 Thresholded

Three consecutive thresholds are used, each more liberal than the previous:

  • p_FDR < 0.05: parcels with mean model fit significantly greater than 0 following FDR correction over all parcels (separately per model and trialtype set)
  • p_uncorr < 0.05: parcels with mean model fit significantly greater than 0 (uncorrected)
  • t_stat > 0: parcels with mean model fit numerically greater than zero

2.2.1 Common scale

dat_sum$is_in_thresh_fdr <- dat_sum$p_fdr < 0.05
dat_sum$is_in_thresh_p <- dat_sum$p < 0.05
dat_sum$is_in_thresh_sign <- dat_sum$t_stat > 0

thresholds <- c("p_fdr < 0.05", "p_uncorr < 0.05", "t_stat > 0")
thresholds_nms <- c("is_in_thresh_fdr", "is_in_thresh_p", "is_in_thresh_sign")

for (ttype_subset_i in seq_along(ttype_subsets)) {

  for (thresh_i in seq_along(thresholds)) {
  
    threshold_nm <- thresholds_nms[thresh_i]
  
    for (stat_i in seq_along(statistics)) {
  
      dat_sum$thresh <- as.numeric(ifelse(dat_sum[[threshold_nm]], dat_sum[[statistics[stat_i]]], NA))
  
      p <- dat_sum %>%
        filter(ttype_subset == ttype_subsets[ttype_subset_i]) %>%
        ggplot() +
        geom_brain(
          aes(fill = thresh),
          atlas = atlas, position = position_brain(side ~ hemi)
          ) +
        scale_fill_viridis_c(
          option = "magma", na.value = "grey",
          breaks = scales::extended_breaks(4)
          ) +
        facet_grid(rows = vars(term), cols = vars(session)) +
        theme_surface +
        labs(
          title = paste0(
            titles[stat_i], " ", ttype_subsets[ttype_subset_i], ": thresholded at ", thresholds[thresh_i]
            ), fill = NULL
          )
  
      print(p)
      
    }
  
  }

}

3 Tables

3.1 Baseline+Proactive conjunction analysis

Parcels that are FDR-significant within both baseline and proactive sessions. Separately for bias and pc50 items.

3.1.1 FDR corrected whole-cortex

for (ttypesub in ttype_subsets) {
  
  sig_both <- dat_sum %>% 
    filter(p_fdr < 0.05, ttype_subset == ttypesub, session %in% c("baseline", "proactive")) %>% 
    group_by(term, region) %>% 
    summarize(is_sig_both = n() == 2) %>%
    filter(is_sig_both)
  
  d <- enlist(c("target", "distractor", "incongruency"))
  for (model in names(d)) {
    
    regions <- sig_both$region[sig_both$term == model & sig_both$term == model]
    
    d[[model]] <- dat_sum %>%
      filter(ttype_subset == ttypesub, term == model, region %in% regions) %>%
      ungroup %>%
      select(region, session, m, t_stat, p) %>%
      arrange(region, session)
  
  }
  
  print(lapply(names(d), function(x) kable(d[[x]], caption = paste0(ttypesub, " ", x))))
  
}
## `summarise()` has grouped output by 'term'. You can override using the `.groups` argument.

[[1]]

pc50 target
region session m t_stat p
R_3b baseline 0.0136747 4.2950622 0.0000313
R_3b pro - bas -0.0013983 -0.2850443 0.7765603
R_3b proactive 0.0122765 3.4623801 0.0004879
R_6ma baseline 0.0148989 3.6927110 0.0002354
R_6ma pro - bas -0.0010641 -0.2084019 0.8355983
R_6ma proactive 0.0138348 3.4051636 0.0005824
R_A5 baseline 0.0194050 4.0869374 0.0000639
R_A5 pro - bas -0.0070905 -1.2115724 0.2302736
R_A5 proactive 0.0123146 3.3820439 0.0006253

[[2]]

pc50 distractor
region session m t_stat p
L_4 baseline 0.0088508 4.2325022 0.0000389
L_4 pro - bas 0.0039386 1.0222881 0.3106170
L_4 proactive 0.0127894 4.1521684 0.0000512
R_4 baseline 0.0079654 3.4089821 0.0005756
R_4 pro - bas 0.0018181 0.4886019 0.6268474
R_4 proactive 0.0097836 3.7281412 0.0002099

[[3]]

pc50 incongruency
region session m t_stat p
L_1 baseline 0.0250155 3.2775181 0.0008593
L_1 pro - bas -0.0019012 -0.1905058 0.8495352
L_1 proactive 0.0231143 3.0179718 0.0018447
L_31a baseline 0.0263768 2.0181354 0.0239547
L_31a pro - bas 0.0246165 1.3497223 0.1820127
L_31a proactive 0.0509932 3.5906716 0.0003261
L_31pv baseline 0.0403942 3.6714483 0.0002520
L_31pv pro - bas -0.0127901 -0.7825944 0.4368443
L_31pv proactive 0.0276041 2.7280038 0.0041392
L_3a baseline 0.0252639 3.1001865 0.0014540
L_3a pro - bas 0.0045528 0.3546220 0.7240762
L_3a proactive 0.0298167 3.4889843 0.0004491
L_4 baseline 0.0327898 4.1661319 0.0000488
L_4 pro - bas 0.0104880 0.9147621 0.3638606
L_4 proactive 0.0432778 5.5986734 0.0000003
L_43 baseline 0.0337807 2.7859792 0.0035356
L_43 pro - bas -0.0075529 -0.4890418 0.6265377
L_43 proactive 0.0262278 2.8977921 0.0025942
L_44 baseline 0.0396427 3.5990727 0.0003175
L_44 pro - bas -0.0035841 -0.2367109 0.8136614
L_44 proactive 0.0360586 3.4970868 0.0004379
L_45 baseline 0.0426258 5.1225961 0.0000016
L_45 pro - bas -0.0165040 -1.3088533 0.1954148
L_45 proactive 0.0261218 2.9124640 0.0024895
L_46 baseline 0.0246938 2.7066229 0.0043846
L_46 pro - bas 0.0019397 0.1307783 0.8963741
L_46 proactive 0.0266335 2.3416812 0.0112126
L_55b baseline 0.0307700 2.9457176 0.0022666
L_55b pro - bas -0.0027459 -0.2091955 0.8349815
L_55b proactive 0.0280240 3.0824748 0.0015310
L_6a baseline 0.0520084 5.3578070 0.0000007
L_6a pro - bas -0.0133346 -1.0345753 0.3048859
L_6a proactive 0.0386738 3.8210171 0.0001551
L_6ma baseline 0.0342609 3.2074755 0.0010598
L_6ma pro - bas 0.0043704 0.3030088 0.7628975
L_6ma proactive 0.0386313 4.1590328 0.0000500
L_6mp baseline 0.0284588 3.9307709 0.0001080
L_6mp pro - bas -0.0091140 -0.7734253 0.4422097
L_6mp proactive 0.0193449 2.1664022 0.0170665
L_6r baseline 0.0542686 5.2269459 0.0000011
L_6r pro - bas -0.0138833 -1.0842855 0.2824367
L_6r proactive 0.0403853 4.9424939 0.0000031
L_6v baseline 0.0493127 4.4708149 0.0000169
L_6v pro - bas -0.0023553 -0.1868891 0.8523578
L_6v proactive 0.0469575 5.0701595 0.0000019
L_7m baseline 0.0357428 3.4012227 0.0005895
L_7m pro - bas -0.0007814 -0.0512561 0.9592862
L_7m proactive 0.0349614 3.0353259 0.0017548
L_7Pm baseline 0.0406123 2.9788018 0.0020634
L_7Pm pro - bas 0.0092868 0.5424678 0.5894409
L_7Pm proactive 0.0498991 4.2204511 0.0000405
L_8Ad baseline 0.0357119 4.0761918 0.0000663
L_8Ad pro - bas -0.0092451 -0.7578026 0.4514399
L_8Ad proactive 0.0264668 3.4193251 0.0005575
L_8BM baseline 0.0332537 3.0891690 0.0015014
L_8BM pro - bas 0.0093455 0.6714989 0.5043971
L_8BM proactive 0.0425992 4.5949320 0.0000109
L_8C baseline 0.0675154 5.6651835 0.0000002
L_8C pro - bas -0.0120092 -0.8188014 0.4160359
L_8C proactive 0.0555062 6.2544948 0.0000000
L_9-46d baseline 0.0221135 2.6532507 0.0050565
L_9-46d pro - bas 0.0004054 0.0355322 0.9717695
L_9-46d proactive 0.0225189 2.2732554 0.0132431
L_9a baseline 0.0348476 3.4304778 0.0005386
L_9a pro - bas -0.0146061 -1.1011688 0.2750799
L_9a proactive 0.0202415 2.4612645 0.0083208
L_9p baseline 0.0329332 3.5487265 0.0003723
L_9p pro - bas -0.0077115 -0.5790275 0.5646685
L_9p proactive 0.0252217 2.5603641 0.0064531
L_AIP baseline 0.0795479 7.4639684 0.0000000
L_AIP pro - bas -0.0433342 -3.1774166 0.0023175
L_AIP proactive 0.0362137 3.7355071 0.0002050
L_d32 baseline 0.0337588 3.6433829 0.0002757
L_d32 pro - bas 0.0027145 0.1925211 0.8479632
L_d32 proactive 0.0364733 4.0245498 0.0000789
L_FEF baseline 0.0358484 3.5799895 0.0003373
L_FEF pro - bas -0.0034715 -0.2522377 0.8016916
L_FEF proactive 0.0323769 3.1894405 0.0011182
L_FOP4 baseline 0.0584673 5.6159944 0.0000002
L_FOP4 pro - bas -0.0341188 -2.7237538 0.0083738
L_FOP4 proactive 0.0243484 2.6241083 0.0054618
L_FOP5 baseline 0.0322949 3.1041045 0.0014375
L_FOP5 pro - bas -0.0091393 -0.6766664 0.5011345
L_FOP5 proactive 0.0231556 2.7934260 0.0034643
L_FST baseline 0.0524240 4.5424812 0.0000131
L_FST pro - bas -0.0218283 -1.5351660 0.1298308
L_FST proactive 0.0305956 3.1146965 0.0013936
L_i6-8 baseline 0.0533561 4.3524360 0.0000256
L_i6-8 pro - bas -0.0251549 -1.4547021 0.1507961
L_i6-8 proactive 0.0282012 2.8378694 0.0030652
L_IFJa baseline 0.0806943 6.4804739 0.0000000
L_IFJa pro - bas -0.0430783 -2.4116645 0.0188546
L_IFJa proactive 0.0376161 3.2672187 0.0008863
L_IFJp baseline 0.0544792 4.2628561 0.0000350
L_IFJp pro - bas -0.0155944 -0.8962073 0.3736087
L_IFJp proactive 0.0388848 3.3563321 0.0006765
L_IFSa baseline 0.0773138 6.0667898 0.0000000
L_IFSa pro - bas -0.0138961 -0.8734622 0.3857818
L_IFSa proactive 0.0634176 5.4528686 0.0000005
L_IFSp baseline 0.0872288 6.1192201 0.0000000
L_IFSp pro - bas -0.0461277 -2.4839035 0.0157115
L_IFSp proactive 0.0411011 3.2150464 0.0010362
L_IP0 baseline 0.0464968 3.1684795 0.0011898
L_IP0 pro - bas 0.0154885 0.8557017 0.3954577
L_IP0 proactive 0.0619853 5.2456400 0.0000010
L_IP1 baseline 0.1023714 7.9336086 0.0000000
L_IP1 pro - bas -0.0200309 -1.1819054 0.2417566
L_IP1 proactive 0.0823405 6.9514309 0.0000000
L_IP2 baseline 0.0674154 5.7595818 0.0000001
L_IP2 pro - bas -0.0212954 -1.4066845 0.1645139
L_IP2 proactive 0.0461200 3.6039362 0.0003127
L_IPS1 baseline 0.0431928 3.7844046 0.0001749
L_IPS1 pro - bas -0.0035816 -0.2405617 0.8106885
L_IPS1 proactive 0.0396111 3.9137654 0.0001143
L_LIPd baseline 0.0916975 7.0928118 0.0000000
L_LIPd pro - bas -0.0365356 -2.2010701 0.0314632
L_LIPd proactive 0.0551618 5.0105870 0.0000024
L_LIPv baseline 0.0518520 4.3080600 0.0000299
L_LIPv pro - bas -0.0286405 -1.9044072 0.0614994
L_LIPv proactive 0.0232114 2.2402105 0.0143355
L_MIP baseline 0.0783998 6.6345078 0.0000000
L_MIP pro - bas -0.0217185 -1.5073826 0.1367914
L_MIP proactive 0.0566814 5.3318480 0.0000007
L_p47r baseline 0.0269712 2.6821878 0.0046814
L_p47r pro - bas 0.0015626 0.1201300 0.9047686
L_p47r proactive 0.0285338 2.7202648 0.0042265
L_p9-46v baseline 0.0658649 5.1551157 0.0000014
L_p9-46v pro - bas -0.0134901 -0.9150329 0.3637195
L_p9-46v proactive 0.0523747 4.7615837 0.0000060
L_PCV baseline 0.0317700 2.9714312 0.0021071
L_PCV pro - bas -0.0062594 -0.4124103 0.6814613
L_PCV proactive 0.0255106 2.6104651 0.0056615
L_PEF baseline 0.0726216 5.5553934 0.0000003
L_PEF pro - bas -0.0369481 -2.1419290 0.0361352
L_PEF proactive 0.0356735 2.5157159 0.0072418
L_PF baseline 0.0629092 6.7901597 0.0000000
L_PF pro - bas -0.0253248 -1.7703841 0.0815793
L_PF proactive 0.0375843 3.7706636 0.0001829
L_PFcm baseline 0.0453176 3.6612469 0.0002604
L_PFcm pro - bas -0.0038876 -0.2084954 0.8355256
L_PFcm proactive 0.0414299 2.9887334 0.0020057
L_PFm baseline 0.0377244 3.7570408 0.0001911
L_PFm pro - bas -0.0026710 -0.1848624 0.8539404
L_PFm proactive 0.0350533 3.4394685 0.0005238
L_PFop baseline 0.0399872 4.0419563 0.0000744
L_PFop pro - bas 0.0058528 0.4649410 0.6436026
L_PFop proactive 0.0458400 5.4558858 0.0000005
L_PFt baseline 0.0537449 4.4772111 0.0000165
L_PFt pro - bas -0.0171331 -1.0229484 0.3103071
L_PFt proactive 0.0366118 3.1915988 0.0011110
L_PGp baseline 0.0407329 3.6077834 0.0003089
L_PGp pro - bas -0.0140423 -0.8345765 0.4071605
L_PGp proactive 0.0266906 2.3980846 0.0097524
L_PGs baseline 0.0278521 2.5418416 0.0067703
L_PGs pro - bas 0.0007523 0.0577009 0.9541724
L_PGs proactive 0.0286044 3.0026635 0.0019274
L_PH baseline 0.0475936 4.2958348 0.0000312
L_PH pro - bas -0.0192685 -1.3636218 0.1776175
L_PH proactive 0.0283251 3.4914997 0.0004456
L_PHA3 baseline 0.0224219 2.3941125 0.0098494
L_PHA3 pro - bas 0.0051542 0.4676927 0.6416441
L_PHA3 proactive 0.0275761 3.0840586 0.0015239
L_PHT baseline 0.0483927 5.1854731 0.0000013
L_PHT pro - bas -0.0122964 -0.8730645 0.3859968
L_PHT proactive 0.0360964 3.4607528 0.0004904
L_POS1 baseline 0.0330198 2.5832233 0.0060802
L_POS1 pro - bas -0.0067121 -0.3899887 0.6978815
L_POS1 proactive 0.0263077 2.2798181 0.0130351
L_POS2 baseline 0.0550723 5.0799535 0.0000019
L_POS2 pro - bas -0.0162740 -1.1270345 0.2640710
L_POS2 proactive 0.0387982 3.3295183 0.0007341
L_PSL baseline 0.0417531 4.5745974 0.0000117
L_PSL pro - bas -0.0021277 -0.1424264 0.8872051
L_PSL proactive 0.0396253 3.7749706 0.0001803
L_RI baseline 0.0392052 4.5999912 0.0000107
L_RI pro - bas -0.0195815 -1.6950803 0.0950775
L_RI proactive 0.0196238 2.2463353 0.0141272
L_RSC baseline 0.0258053 2.1931416 0.0160285
L_RSC pro - bas 0.0006728 0.0491730 0.9609395
L_RSC proactive 0.0264781 2.1643232 0.0171496
L_SCEF baseline 0.0554164 6.5583199 0.0000000
L_SCEF pro - bas 0.0113341 0.9875780 0.3271979
L_SCEF proactive 0.0667505 8.2820443 0.0000000
L_SFL baseline 0.0542923 5.2660745 0.0000009
L_SFL pro - bas -0.0182674 -1.3188054 0.1920848
L_SFL proactive 0.0360249 3.7469911 0.0001975
L_STSda baseline 0.0281670 2.7207133 0.0042214
L_STSda pro - bas -0.0075148 -0.5553736 0.5806376
L_STSda proactive 0.0206522 2.3820578 0.0101489
L_STV baseline 0.0322208 2.8003133 0.0033995
L_STV pro - bas -0.0034371 -0.2261130 0.8218573
L_STV proactive 0.0287837 2.8913151 0.0026416
L_TPOJ1 baseline 0.0527931 4.8358571 0.0000046
L_TPOJ1 pro - bas -0.0018332 -0.1226753 0.9027610
L_TPOJ1 proactive 0.0509599 5.0529398 0.0000021
L_TPOJ3 baseline 0.0375986 2.7971176 0.0034294
L_TPOJ3 pro - bas -0.0118325 -0.7007359 0.4860894
L_TPOJ3 proactive 0.0257661 2.2476134 0.0140841
L_V3 baseline 0.0358501 3.9140086 0.0001142
L_V3 pro - bas -0.0022455 -0.1813906 0.8566527
L_V3 proactive 0.0336046 4.1499906 0.0000516
L_V4 baseline 0.0280787 2.8152002 0.0032631
L_V4 pro - bas -0.0078472 -0.5973130 0.5524737
L_V4 proactive 0.0202315 2.3245777 0.0116923
R_23c baseline 0.0173074 2.1802555 0.0165216
R_23c pro - bas 0.0149526 1.4993257 0.1388642
R_23c proactive 0.0322600 4.4148567 0.0000206
R_31pd baseline 0.0457540 3.6014629 0.0003151
R_31pd pro - bas -0.0198064 -1.1679375 0.2473037
R_31pd proactive 0.0259476 2.3223216 0.0117569
R_31pv baseline 0.0282672 2.4979196 0.0075797
R_31pv pro - bas -0.0010518 -0.0607643 0.9517424
R_31pv proactive 0.0272154 2.2128773 0.0152983
R_3a baseline 0.0246922 2.7811763 0.0035824
R_3a pro - bas 0.0098012 0.7835488 0.4362880
R_3a proactive 0.0344934 4.6857796 0.0000079
R_3b baseline 0.0224102 2.9675417 0.0021306
R_3b pro - bas 0.0000109 0.0009842 0.9992179
R_3b proactive 0.0224211 3.1683479 0.0011902
R_4 baseline 0.0293674 4.5716356 0.0000118
R_4 pro - bas 0.0022981 0.2330836 0.8164643
R_4 proactive 0.0316655 4.4943172 0.0000156
R_43 baseline 0.0198482 1.9996649 0.0249615
R_43 pro - bas 0.0219503 1.6946413 0.0951613
R_43 proactive 0.0417985 4.1721196 0.0000478
R_44 baseline 0.0280822 2.3358055 0.0113754
R_44 pro - bas 0.0065236 0.3895087 0.6982347
R_44 proactive 0.0346058 3.3557443 0.0006777
R_45 baseline 0.0353285 3.2198493 0.0010215
R_45 pro - bas -0.0051494 -0.3014775 0.7640592
R_45 proactive 0.0301791 2.5385184 0.0068287
R_46 baseline 0.0279360 3.5122343 0.0004176
R_46 pro - bas -0.0061214 -0.5463945 0.5867558
R_46 proactive 0.0218146 2.7395948 0.0040114
R_6a baseline 0.0353947 4.3378584 0.0000270
R_6a pro - bas -0.0086521 -0.7578622 0.4514045
R_6a proactive 0.0267426 2.7354884 0.0040563
R_6ma baseline 0.0430124 4.6204896 0.0000099
R_6ma pro - bas 0.0193867 1.5580643 0.1243083
R_6ma proactive 0.0623991 7.3603747 0.0000000
R_6mp baseline 0.0190499 3.1160971 0.0013879
R_6mp pro - bas 0.0059751 0.6330840 0.5290071
R_6mp proactive 0.0250250 3.1042547 0.0014368
R_6r baseline 0.0558549 5.3014662 0.0000008
R_6r pro - bas -0.0173321 -1.4088027 0.1638891
R_6r proactive 0.0385228 4.7167279 0.0000070
R_6v baseline 0.0433786 4.0437209 0.0000740
R_6v pro - bas -0.0026740 -0.1744796 0.8620570
R_6v proactive 0.0407046 4.3851102 0.0000229
R_7PL baseline 0.0322550 2.3269006 0.0116261
R_7PL pro - bas 0.0095324 0.4979737 0.6202644
R_7PL proactive 0.0417874 3.2167599 0.0010309
R_7Pm baseline 0.0261672 2.3077895 0.0121808
R_7Pm pro - bas 0.0010661 0.0656804 0.9478436
R_7Pm proactive 0.0272333 2.7189178 0.0042419
R_8BL baseline 0.0303858 3.6253940 0.0002920
R_8BL pro - bas -0.0034594 -0.2748774 0.7843244
R_8BL proactive 0.0269264 2.9122600 0.0024909
R_8BM baseline 0.0333144 3.2335994 0.0009804
R_8BM pro - bas 0.0108933 0.8639401 0.3909509
R_8BM proactive 0.0442077 4.8084055 0.0000050
R_a32pr baseline 0.0270071 2.3653583 0.0105774
R_a32pr pro - bas 0.0065047 0.4342448 0.6656182
R_a32pr proactive 0.0335118 3.2264370 0.0010016
R_d23ab baseline 0.0462789 3.0778101 0.0015519
R_d23ab pro - bas 0.0014143 0.0717626 0.9430218
R_d23ab proactive 0.0476932 3.5286503 0.0003966
R_d32 baseline 0.0320152 3.2011853 0.0010798
R_d32 pro - bas -0.0137954 -1.1525903 0.2535032
R_d32 proactive 0.0182198 2.3209984 0.0117950
R_FEF baseline 0.0229212 2.6306115 0.0053688
R_FEF pro - bas 0.0093026 0.7110095 0.4797447
R_FEF proactive 0.0322238 3.5785726 0.0003388
R_FOP1 baseline 0.0453015 4.1311364 0.0000550
R_FOP1 pro - bas -0.0051314 -0.3174846 0.7519426
R_FOP1 proactive 0.0401702 3.1945272 0.0011014
R_FOP4 baseline 0.0320719 2.6896106 0.0045893
R_FOP4 pro - bas 0.0029333 0.2019794 0.8405941
R_FOP4 proactive 0.0350052 3.0885870 0.0015040
R_IFJa baseline 0.0581607 4.1261433 0.0000559
R_IFJa pro - bas -0.0282809 -1.8821788 0.0645069
R_IFJa proactive 0.0298797 2.8041546 0.0033638
R_IFJp baseline 0.0421848 2.7223415 0.0042029
R_IFJp pro - bas -0.0157123 -0.8312603 0.4090166
R_IFJp proactive 0.0264726 2.4643302 0.0082564
R_IFSa baseline 0.0228276 2.5137140 0.0072791
R_IFSa pro - bas 0.0019892 0.1597799 0.8735739
R_IFSa proactive 0.0248168 2.8961656 0.0026060
R_IFSp baseline 0.0360302 3.4389260 0.0005247
R_IFSp pro - bas -0.0110350 -0.7677872 0.4455280
R_IFSp proactive 0.0249952 2.5805372 0.0061230
R_IP0 baseline 0.0581368 4.8214525 0.0000048
R_IP0 pro - bas 0.0027214 0.1633891 0.8707435
R_IP0 proactive 0.0608582 4.7631274 0.0000059
R_IP1 baseline 0.0620895 5.5476399 0.0000003
R_IP1 pro - bas -0.0073704 -0.4623896 0.6454206
R_IP1 proactive 0.0547190 4.3960019 0.0000220
R_LIPd baseline 0.0543875 5.1997642 0.0000012
R_LIPd pro - bas -0.0197143 -1.1390711 0.2590555
R_LIPd proactive 0.0346732 2.5757677 0.0061997
R_MIP baseline 0.0400735 3.4293939 0.0005404
R_MIP pro - bas -0.0147576 -1.0278038 0.3080354
R_MIP proactive 0.0253159 2.7221350 0.0042052
R_p32pr baseline 0.0320380 3.1308331 0.0013293
R_p32pr pro - bas -0.0088120 -0.6211953 0.5367484
R_p32pr proactive 0.0232260 2.5360839 0.0068718
R_p9-46v baseline 0.0217215 2.2837716 0.0129113
R_p9-46v pro - bas 0.0108279 0.9806992 0.3305525
R_p9-46v proactive 0.0325494 3.9455501 0.0001028
R_PFm baseline 0.0239998 3.3152090 0.0007667
R_PFm pro - bas 0.0103038 1.0038273 0.3193637
R_PFm proactive 0.0343036 3.9354076 0.0001063
R_PGi baseline 0.0515191 5.1995900 0.0000012
R_PGi pro - bas -0.0227865 -1.7961116 0.0773492
R_PGi proactive 0.0287327 3.1955388 0.0010981
R_PGp baseline 0.0428454 4.3297395 0.0000277
R_PGp pro - bas -0.0193038 -1.4175827 0.1613189
R_PGp proactive 0.0235416 2.3028962 0.0123266
R_PGs baseline 0.0346951 3.5298787 0.0003951
R_PGs pro - bas -0.0117933 -0.8651640 0.3902841
R_PGs proactive 0.0229018 2.1958271 0.0159274
R_PH baseline 0.0503501 4.3746103 0.0000237
R_PH pro - bas -0.0257769 -1.7917309 0.0780563
R_PH proactive 0.0245732 2.4366043 0.0088554
R_POS2 baseline 0.0451087 3.8731729 0.0001307
R_POS2 pro - bas -0.0228003 -1.6413463 0.1057892
R_POS2 proactive 0.0223084 2.5381043 0.0068360
R_ProS baseline 0.0350273 3.1308786 0.0013291
R_ProS pro - bas -0.0056638 -0.2933605 0.7702263
R_ProS proactive 0.0293635 2.2187903 0.0150854
R_RI baseline 0.0238294 2.2793175 0.0130509
R_RI pro - bas -0.0005509 -0.0374870 0.9702172
R_RI proactive 0.0232785 2.3069240 0.0122065
R_s6-8 baseline 0.0380854 2.8338935 0.0030991
R_s6-8 pro - bas 0.0059065 0.3668575 0.7149744
R_s6-8 proactive 0.0439919 3.5134776 0.0004160
R_SCEF baseline 0.0321544 3.1924698 0.0011082
R_SCEF pro - bas -0.0004331 -0.0362714 0.9711825
R_SCEF proactive 0.0317213 3.3343443 0.0007234
R_STSda baseline 0.0271693 3.0596006 0.0016360
R_STSda pro - bas -0.0034708 -0.2890468 0.7735099
R_STSda proactive 0.0236985 2.5941518 0.0059090
R_TPOJ1 baseline 0.0176715 1.9388050 0.0285401
R_TPOJ1 pro - bas 0.0103060 0.8031345 0.4249653
R_TPOJ1 proactive 0.0279775 3.1695947 0.0011859
R_TPOJ2 baseline 0.0223106 2.2621555 0.0136015
R_TPOJ2 pro - bas 0.0083759 0.6435096 0.5222667
R_TPOJ2 proactive 0.0306865 3.9801317 0.0000916
R_V2 baseline 0.0321044 3.9796844 0.0000917
R_V2 pro - bas -0.0096601 -0.8160976 0.4175688
R_V2 proactive 0.0224443 2.9982247 0.0019521
R_v23ab baseline 0.0424358 3.7800550 0.0001774
R_v23ab pro - bas -0.0136777 -0.7967892 0.4286142
R_v23ab proactive 0.0287581 2.1313907 0.0185142
R_V3 baseline 0.0402717 4.3378285 0.0000270
R_V3 pro - bas -0.0118450 -0.9340413 0.3539062
R_V3 proactive 0.0284267 3.4485041 0.0005094
R_V3CD baseline 0.0606120 4.3821135 0.0000231
R_V3CD pro - bas -0.0344533 -1.8620824 0.0673321
R_V3CD proactive 0.0261587 2.1993893 0.0157941
R_V4 baseline 0.0297084 2.9977525 0.0019547
R_V4 pro - bas -0.0072166 -0.5800767 0.5639652
R_V4 proactive 0.0224918 2.7031072 0.0044262
## `summarise()` has grouped output by 'term'. You can override using the `.groups` argument.

[[1]]

bias target
region session m t_stat p
L_4 baseline 0.0105178 4.4265750 0.0000198
L_4 pro - bas -0.0050921 -1.8196064 0.0736476
L_4 proactive 0.0054256 4.0708312 0.0000675
L_IFJp baseline 0.0141939 3.0546861 0.0016595
L_IFJp pro - bas -0.0046507 -0.9451944 0.3482287
L_IFJp proactive 0.0095432 3.4586928 0.0004935
L_IP1 baseline 0.0133677 2.6030923 0.0057721
L_IP1 pro - bas -0.0048805 -0.9738529 0.3339139
L_IP1 proactive 0.0084872 3.0006677 0.0019385
L_LIPd baseline 0.0161102 3.6840644 0.0002420
L_LIPd pro - bas -0.0068371 -1.3793460 0.1727433
L_LIPd proactive 0.0092732 3.2371084 0.0009701
L_RI baseline 0.0099509 2.9156557 0.0024673
L_RI pro - bas -0.0037897 -0.9180987 0.3621251
L_RI proactive 0.0061612 2.9726357 0.0020999
L_SCEF baseline 0.0187824 5.0391057 0.0000022
L_SCEF pro - bas -0.0114097 -2.9444209 0.0045499
L_SCEF proactive 0.0073728 4.7407686 0.0000064
L_STSdp baseline 0.0113015 2.9568042 0.0021965
L_STSdp pro - bas -0.0038761 -0.8747923 0.3850632
L_STSdp proactive 0.0074254 2.9689941 0.0021218
R_1 baseline 0.0079756 2.8399664 0.0030475
R_1 pro - bas 0.0001187 0.0353203 0.9719378
R_1 proactive 0.0080943 4.2243711 0.0000400
R_3b baseline 0.0103628 3.7531312 0.0001936
R_3b pro - bas -0.0018198 -0.5917747 0.5561533
R_3b proactive 0.0085430 5.3553529 0.0000007
R_V3 baseline 0.0111607 4.0831468 0.0000647
R_V3 pro - bas -0.0047249 -1.4602386 0.1492735
R_V3 proactive 0.0064358 4.1727920 0.0000477
R_V8 baseline 0.0142793 2.5763219 0.0061907
R_V8 pro - bas -0.0001645 -0.0252239 0.9799574
R_V8 proactive 0.0141148 3.6538804 0.0002666

[[2]]

bias distractor
region session m t_stat p
L_FFC baseline 0.0130309 2.7385069 0.0040233
L_FFC pro - bas -0.0067809 -1.2616702 0.2117936
L_FFC proactive 0.0062500 3.1214043 0.0013665
L_V1 baseline 0.0187363 4.9167257 0.0000034
L_V1 pro - bas -0.0027061 -0.7707996 0.4437532
L_V1 proactive 0.0160302 5.5484856 0.0000003
L_V2 baseline 0.0174852 4.3595060 0.0000250
L_V2 pro - bas -0.0007692 -0.1789910 0.8585284
L_V2 proactive 0.0167161 5.9017659 0.0000001
L_V3 baseline 0.0247319 4.9458045 0.0000030
L_V3 pro - bas -0.0030339 -0.5672934 0.5725632
L_V3 proactive 0.0216981 5.5267734 0.0000003
L_V4 baseline 0.0117444 3.0059468 0.0019094
L_V4 pro - bas -0.0008110 -0.1910941 0.8490763
L_V4 proactive 0.0109334 4.5474591 0.0000129
R_V1 baseline 0.0141985 3.9072196 0.0001168
R_V1 pro - bas -0.0024333 -0.6304332 0.5307281
R_V1 proactive 0.0117652 5.2978488 0.0000008
R_V2 baseline 0.0164023 5.4195061 0.0000005
R_V2 pro - bas -0.0021291 -0.6132187 0.5419749
R_V2 proactive 0.0142732 6.2546568 0.0000000
R_V3 baseline 0.0264743 6.3471662 0.0000000
R_V3 pro - bas -0.0107110 -2.7945169 0.0069079
R_V3 proactive 0.0157633 6.3680933 0.0000000
R_V4 baseline 0.0193733 4.8183405 0.0000049
R_V4 pro - bas -0.0092954 -2.2422150 0.0285341
R_V4 proactive 0.0100780 4.8426600 0.0000044

[[3]]

bias incongruency
region session m t_stat p
L_2 baseline 0.0440436 6.3732008 0.0000000
L_2 pro - bas -0.0305906 -3.4703800 0.0009518
L_2 proactive 0.0134530 2.5567555 0.0065139
L_44 baseline 0.0627007 7.0598226 0.0000000
L_44 pro - bas -0.0297864 -2.8607772 0.0057531
L_44 proactive 0.0329143 4.2487652 0.0000367
L_6a baseline 0.0776408 8.4562962 0.0000000
L_6a pro - bas -0.0550228 -5.5732311 0.0000006
L_6a proactive 0.0226179 3.3316391 0.0007294
L_6ma baseline 0.0652127 5.8885810 0.0000001
L_6ma pro - bas -0.0418598 -3.2501915 0.0018657
L_6ma proactive 0.0233528 2.6080788 0.0056971
L_6r baseline 0.0822818 8.3356220 0.0000000
L_6r pro - bas -0.0548245 -5.0351371 0.0000044
L_6r proactive 0.0274573 3.8635094 0.0001349
L_6v baseline 0.0709255 6.7132234 0.0000000
L_6v pro - bas -0.0362703 -3.1768357 0.0023215
L_6v proactive 0.0346552 4.2020626 0.0000432
L_7Am baseline 0.0333142 3.0042699 0.0019186
L_7Am pro - bas -0.0124902 -0.9659339 0.3378300
L_7Am proactive 0.0208240 3.4701379 0.0004763
L_8BM baseline 0.0782112 8.3394970 0.0000000
L_8BM pro - bas -0.0458588 -3.9754078 0.0001861
L_8BM proactive 0.0323524 4.8491358 0.0000043
L_8C baseline 0.1298254 12.3899507 0.0000000
L_8C pro - bas -0.1043779 -8.3166818 0.0000000
L_8C proactive 0.0254474 3.8949796 0.0001216
L_a24pr baseline 0.0313433 3.4987423 0.0004356
L_a24pr pro - bas -0.0058798 -0.4622512 0.6455193
L_a24pr proactive 0.0254635 2.7180182 0.0042522
L_AIP baseline 0.1188968 10.4426462 0.0000000
L_AIP pro - bas -0.0851012 -6.9132468 0.0000000
L_AIP proactive 0.0337956 4.0800821 0.0000654
L_FEF baseline 0.0685446 8.1419616 0.0000000
L_FEF pro - bas -0.0374198 -3.5607511 0.0007170
L_FEF proactive 0.0311248 3.7460637 0.0001981
L_FOP4 baseline 0.0946682 8.6944171 0.0000000
L_FOP4 pro - bas -0.0771766 -6.9153588 0.0000000
L_FOP4 proactive 0.0174916 2.7649000 0.0037450
L_FOP5 baseline 0.0756334 5.9784118 0.0000001
L_FOP5 pro - bas -0.0450335 -3.3219886 0.0015022
L_FOP5 proactive 0.0305998 4.4714420 0.0000169
L_i6-8 baseline 0.1131245 8.8652137 0.0000000
L_i6-8 pro - bas -0.0813786 -5.6286842 0.0000005
L_i6-8 proactive 0.0317458 3.4697455 0.0004768
L_IFJa baseline 0.1037815 9.0227344 0.0000000
L_IFJa pro - bas -0.0804934 -5.3371120 0.0000014
L_IFJa proactive 0.0232881 2.6924533 0.0045545
L_IFJp baseline 0.1035532 7.4976211 0.0000000
L_IFJp pro - bas -0.0602202 -3.5513929 0.0007384
L_IFJp proactive 0.0433329 4.3601015 0.0000250
L_IFSa baseline 0.1044481 9.3324104 0.0000000
L_IFSa pro - bas -0.0585230 -4.4385288 0.0000379
L_IFSa proactive 0.0459252 4.2311259 0.0000390
L_IFSp baseline 0.1128993 8.2850119 0.0000000
L_IFSp pro - bas -0.0821299 -5.2301313 0.0000021
L_IFSp proactive 0.0307695 3.2771138 0.0008603
L_IP0 baseline 0.1144241 8.8548976 0.0000000
L_IP0 pro - bas -0.0844680 -5.9087413 0.0000002
L_IP0 proactive 0.0299561 3.3917379 0.0006069
L_IP1 baseline 0.1605688 12.0679848 0.0000000
L_IP1 pro - bas -0.1002203 -7.0261082 0.0000000
L_IP1 proactive 0.0603485 5.4808039 0.0000004
L_IP2 baseline 0.1274545 9.7923068 0.0000000
L_IP2 pro - bas -0.0893015 -5.6416816 0.0000004
L_IP2 proactive 0.0381529 3.8943427 0.0001218
L_IPS1 baseline 0.1009390 8.2226012 0.0000000
L_IPS1 pro - bas -0.0700892 -5.2939201 0.0000017
L_IPS1 proactive 0.0308498 3.7049640 0.0002262
L_LIPd baseline 0.1374634 10.1247619 0.0000000
L_LIPd pro - bas -0.0827873 -5.8020643 0.0000002
L_LIPd proactive 0.0546761 4.7528706 0.0000062
L_MIP baseline 0.0908872 6.8492150 0.0000000
L_MIP pro - bas -0.0610221 -4.1356028 0.0001083
L_MIP proactive 0.0298652 3.1571654 0.0012302
L_p32pr baseline 0.0671926 8.2854837 0.0000000
L_p32pr pro - bas -0.0433164 -4.5606336 0.0000246
L_p32pr proactive 0.0238762 3.1695565 0.0011860
L_p9-46v baseline 0.1322322 10.0914128 0.0000000
L_p9-46v pro - bas -0.1066311 -8.1073066 0.0000000
L_p9-46v proactive 0.0256011 3.1018548 0.0014469
L_PEF baseline 0.1035129 7.3200266 0.0000000
L_PEF pro - bas -0.0623220 -4.2525318 0.0000725
L_PEF proactive 0.0411910 4.1208549 0.0000570
L_PFcm baseline 0.0765672 6.8889378 0.0000000
L_PFcm pro - bas -0.0323660 -2.1893929 0.0323412
L_PFcm proactive 0.0442012 3.7409945 0.0002014
L_PFop baseline 0.0670375 5.8537530 0.0000001
L_PFop pro - bas -0.0454523 -3.2135967 0.0020814
L_PFop proactive 0.0215852 2.5224304 0.0071179
L_PFt baseline 0.0756435 6.5347537 0.0000000
L_PFt pro - bas -0.0478629 -4.4424054 0.0000374
L_PFt proactive 0.0277806 3.6038501 0.0003127
L_SCEF baseline 0.0865741 11.0773376 0.0000000
L_SCEF pro - bas -0.0483153 -4.8081666 0.0000101
L_SCEF proactive 0.0382588 4.9018576 0.0000036
L_SFL baseline 0.0741188 7.5936974 0.0000000
L_SFL pro - bas -0.0424488 -3.9057987 0.0002346
L_SFL proactive 0.0316701 4.6413327 0.0000092
L_STV baseline 0.0670425 7.0861017 0.0000000
L_STV pro - bas -0.0481076 -4.8721379 0.0000080
L_STV proactive 0.0189350 2.8332554 0.0031046
L_TPOJ1 baseline 0.0657798 5.0801181 0.0000019
L_TPOJ1 pro - bas -0.0441469 -3.3141322 0.0015384
L_TPOJ1 proactive 0.0216329 2.8590668 0.0028902
L_VIP baseline 0.0398614 3.3948238 0.0006012
L_VIP pro - bas -0.0150741 -1.2892347 0.2021057
L_VIP proactive 0.0247873 3.1291338 0.0013359
R_1 baseline 0.0200742 3.5560650 0.0003638
R_1 pro - bas -0.0060326 -0.7516098 0.4551294
R_1 proactive 0.0140416 2.5388565 0.0068228
R_44 baseline 0.0542405 4.7981012 0.0000052
R_44 pro - bas -0.0294597 -2.1933836 0.0320387
R_44 proactive 0.0247808 3.7808990 0.0001769
R_6a baseline 0.0531351 6.9274053 0.0000000
R_6a pro - bas -0.0331047 -3.2455318 0.0018919
R_6a proactive 0.0200304 3.6031985 0.0003134
R_6ma baseline 0.0665931 7.7047138 0.0000000
R_6ma pro - bas -0.0356329 -3.1633420 0.0024159
R_6ma proactive 0.0309602 4.5731446 0.0000118
R_8BM baseline 0.0799758 7.5284278 0.0000000
R_8BM pro - bas -0.0546368 -4.7008176 0.0000149
R_8BM proactive 0.0253390 3.4983964 0.0004361
R_A5 baseline 0.0289726 4.1204854 0.0000570
R_A5 pro - bas -0.0118641 -1.2928625 0.2008557
R_A5 proactive 0.0171085 2.9239308 0.0024105
R_FEF baseline 0.0541432 5.9474122 0.0000001
R_FEF pro - bas -0.0355550 -3.0887013 0.0030070
R_FEF proactive 0.0185882 2.5128892 0.0072945
R_FOP5 baseline 0.0751667 6.8105134 0.0000000
R_FOP5 pro - bas -0.0527226 -4.3004538 0.0000614
R_FOP5 proactive 0.0224441 2.6406497 0.0052282
R_IFJa baseline 0.0707536 5.2205589 0.0000011
R_IFJa pro - bas -0.0467337 -2.8471787 0.0059744
R_IFJa proactive 0.0240199 2.5674762 0.0063350
R_IFJp baseline 0.0758448 5.8816558 0.0000001
R_IFJp pro - bas -0.0435221 -3.2813288 0.0016989
R_IFJp proactive 0.0323227 3.5834295 0.0003337
R_IFSp baseline 0.0889198 7.9356230 0.0000000
R_IFSp pro - bas -0.0594514 -4.8978170 0.0000073
R_IFSp proactive 0.0294684 3.9636692 0.0000968
R_IP2 baseline 0.0789511 6.7677039 0.0000000
R_IP2 pro - bas -0.0574684 -4.0694410 0.0001356
R_IP2 proactive 0.0214827 2.7131062 0.0043088
R_IPS1 baseline 0.1029098 7.5110154 0.0000000
R_IPS1 pro - bas -0.0795760 -5.1250720 0.0000031
R_IPS1 proactive 0.0233338 2.6787166 0.0047250
R_LIPd baseline 0.0766589 6.5330034 0.0000000
R_LIPd pro - bas -0.0496233 -3.4290108 0.0010821
R_LIPd proactive 0.0270356 2.9553860 0.0022054
R_MIP baseline 0.0908465 7.8527802 0.0000000
R_MIP pro - bas -0.0639325 -5.1486826 0.0000029
R_MIP proactive 0.0269140 3.5587276 0.0003608
R_PEF baseline 0.0824335 6.7068928 0.0000000
R_PEF pro - bas -0.0508498 -3.5502529 0.0007411
R_PEF proactive 0.0315838 3.3507349 0.0006882
R_PFt baseline 0.0342334 3.5063255 0.0004254
R_PFt pro - bas -0.0116017 -0.9734372 0.3341187
R_PFt proactive 0.0226317 2.6217711 0.0054955
R_SCEF baseline 0.0524407 5.7620206 0.0000001
R_SCEF pro - bas -0.0258615 -2.1145038 0.0385001
R_SCEF proactive 0.0265791 3.2898747 0.0008278

3.2 Proactive - Baseline difference analysis

Relative to baseline session, 10% of regions with largest absolute change in proactive. Separately for bias and pc50 items and each model. Negative sign indicates baseline session was stronger.

for (ttypesub in ttype_subsets) {
  
  d <- enlist(c("target", "distractor", "incongruency"))
  for (model in names(d)) {
    
    d[[model]] <- dat_sum %>% 
        filter(session == "pro - bas", ttype_subset == ttypesub, term == model) %>%
        mutate(abs_t = abs(t_stat)) %>%
        slice_max(abs_t, prop = 0.1) %>% 
        ungroup %>%
        select(region, m, t_stat, p) %>%
        arrange(t_stat)
      
  }
  
  print(lapply(names(d), function(x) kable(d[[x]], caption = paste0(ttypesub, " ", x))))
  
}

[[1]]

pc50 target
region m t_stat p
R_9m -0.0177344 -3.139649 0.0025906
L_13l -0.0230708 -3.116665 0.0027713
R_31pd -0.0293633 -2.812791 0.0065697
R_PHA3 -0.0230417 -2.636775 0.0105642
R_STV -0.0184597 -2.507935 0.0147756
R_47l -0.0211835 -2.491830 0.0153970
R_5m -0.0204645 -2.290647 0.0253966
L_PGi -0.0183358 -2.281131 0.0259878
L_PBelt -0.0174405 -2.278013 0.0261841
L_8C -0.0161685 -2.251861 0.0278832
L_PEF -0.0194609 -2.246034 0.0282748
L_FFC -0.0133591 -2.108237 0.0390590
L_5mv -0.0135988 -2.075106 0.0421328
L_d32 -0.0158456 -2.055598 0.0440389
L_V6A -0.0213429 -2.033091 0.0463304
R_5mv -0.0132839 -1.950596 0.0556290
R_6mp -0.0112439 -1.939127 0.0570401
R_OP4 -0.0129327 -1.931742 0.0579649
R_LIPd -0.0192152 -1.913118 0.0603537
R_TPOJ3 -0.0168858 -1.878406 0.0650295
L_IFSa -0.0169793 -1.873336 0.0657375
R_i6-8 -0.0177443 -1.856528 0.0681311
L_11l -0.0151238 -1.851810 0.0688161
L_STSda -0.0156874 -1.832186 0.0717274
L_STSdp -0.0109282 -1.818581 0.0738060
R_IFSa -0.0140478 -1.800914 0.0765801
L_LO3 -0.0199444 -1.784926 0.0791653
R_LO1 -0.0163107 -1.783017 0.0794787
R_FOP1 -0.0182600 -1.781797 0.0796797
L_8Av -0.0141846 -1.763928 0.0826705
L_MT 0.0205927 1.863164 0.0671775
L_PoI1 0.0160030 1.875809 0.0653914
R_FOP4 0.0160675 1.962206 0.0542308
L_RI 0.0138888 2.101798 0.0396406
L_PH 0.0158492 2.515669 0.0144853

[[2]]

pc50 distractor
region m t_stat p
R_STV -0.0224215 -3.501822 0.0008628
R_PreS -0.0275640 -2.922989 0.0048337
L_IFJp -0.0157307 -2.403332 0.0192512
L_V8 -0.0206731 -2.355283 0.0216874
R_IFJp -0.0201048 -2.251110 0.0279334
L_10d -0.0165336 -2.232363 0.0292126
R_8BL -0.0176080 -2.163537 0.0343623
R_IFSa -0.0159465 -2.131417 0.0370262
R_TA2 -0.0160526 -2.129899 0.0371565
L_TPOJ3 -0.0196291 -2.076591 0.0419906
R_STSva -0.0148773 -2.028190 0.0468428
L_PHA3 -0.0152003 -2.001255 0.0497468
R_V1 -0.0090104 -1.995736 0.0503606
R_25 -0.0196192 -1.920259 0.0594281
R_TE1a -0.0111865 -1.846049 0.0696602
L_s6-8 -0.0210867 -1.775426 0.0807355
R_a32pr -0.0144108 -1.771386 0.0814110
R_STSvp -0.0147477 -1.770625 0.0815388
R_FOP2 -0.0258548 -1.765611 0.0823849
L_7m -0.0168196 -1.704061 0.0933771
R_FOP5 -0.0126242 -1.689260 0.0961930
R_8C -0.0104230 -1.682074 0.0975850
L_TE2a -0.0080001 -1.663334 0.1012929
R_TE2p -0.0094968 -1.629928 0.1081873
R_6r 0.0115989 1.591153 0.1166615
R_TPOJ2 0.0129277 1.697062 0.0947001
R_d23ab 0.0234043 1.749102 0.0852223
L_6ma 0.0099653 1.775329 0.0807517
R_TPOJ3 0.0168381 1.820733 0.0734739
R_10v 0.0108303 1.861672 0.0673909
R_FEF 0.0157345 2.124398 0.0376318
R_A1 0.0197549 2.280563 0.0260235
L_3b 0.0091341 2.281313 0.0259764
R_MBelt 0.0188773 2.700499 0.0089146
R_SCEF 0.0181595 2.860374 0.0057595

[[3]]

pc50 incongruency
region m t_stat p
L_PGi -0.0461496 -3.489941 0.0008955
L_DVT -0.0507079 -3.298267 0.0016142
L_TPOJ2 -0.0474032 -3.289948 0.0016553
L_AIP -0.0433342 -3.177417 0.0023175
L_FOP4 -0.0341188 -2.723754 0.0083738
L_7PC -0.0320530 -2.703978 0.0088317
L_STSvp -0.0296271 -2.685773 0.0092734
L_IFSp -0.0461277 -2.483903 0.0157115
L_STSdp -0.0332362 -2.479402 0.0158926
R_AIP -0.0355677 -2.451271 0.0170677
L_IFJa -0.0430783 -2.411665 0.0188546
R_IPS1 -0.0326018 -2.401844 0.0193228
L_pOFC -0.0291833 -2.298078 0.0249433
R_i6-8 -0.0324402 -2.271938 0.0265704
R_TF -0.0166461 -2.249656 0.0280308
R_2 -0.0205738 -2.202057 0.0313899
L_LIPd -0.0365356 -2.201070 0.0314632
R_STV -0.0243787 -2.179654 0.0330899
R_VIP -0.0294313 -2.173995 0.0335318
L_PEF -0.0369481 -2.141929 0.0361352
L_9m -0.0212864 -2.090373 0.0406912
R_a24 -0.0320197 -2.047311 0.0448710
L_d23ab -0.0331344 -1.974927 0.0527336
L_STSva -0.0174164 -1.922191 0.0591797
R_V8 -0.0316690 -1.909768 0.0607921
L_LIPv -0.0286405 -1.904407 0.0614994
R_IFJa -0.0282809 -1.882179 0.0645069
R_V3CD -0.0344533 -1.862082 0.0673321
R_9m -0.0179402 -1.822300 0.0732329
R_8Av -0.0246114 -1.820633 0.0734893
R_PGi -0.0227865 -1.796112 0.0773492
R_PH -0.0257769 -1.791731 0.0780563
R_PF -0.0265371 -1.788627 0.0785605
L_V8 -0.0352135 -1.784983 0.0791559
L_TA2 0.0392024 2.623581 0.0109387

[[1]]

bias target
region m t_stat p
L_IFSp -0.0268525 -5.072759 0.0000038
L_8C -0.0180133 -4.853131 0.0000086
R_6a -0.0137063 -4.608107 0.0000208
L_ProS -0.0205743 -3.811706 0.0003199
L_FOP5 -0.0165426 -3.182449 0.0022832
L_PHA1 -0.0193576 -3.125410 0.0027012
L_PFm -0.0140197 -3.109295 0.0028317
L_IP2 -0.0191342 -3.107381 0.0028475
R_IP1 -0.0163877 -2.992851 0.0039646
R_V3A -0.0163378 -2.966849 0.0042695
L_SCEF -0.0114097 -2.944421 0.0045499
L_46 -0.0118663 -2.742847 0.0079525
L_V3 -0.0097893 -2.700141 0.0089232
R_IFSp -0.0121804 -2.679295 0.0094353
R_RI -0.0152330 -2.675069 0.0095424
L_3b -0.0087499 -2.662799 0.0098595
L_3a -0.0085067 -2.604325 0.0115070
R_V1 -0.0087678 -2.570069 0.0125847
R_AIP -0.0125738 -2.565659 0.0127299
R_46 -0.0131433 -2.543272 0.0134907
R_SFL -0.0118203 -2.522875 0.0142194
L_6a -0.0098836 -2.515445 0.0144936
L_p32pr -0.0112593 -2.483936 0.0157102
L_FOP4 -0.0119043 -2.474843 0.0160779
L_IFSa -0.0122984 -2.420395 0.0184469
R_52 -0.0181887 -2.396676 0.0195734
L_9-46d -0.0098769 -2.395763 0.0196179
L_PGi -0.0120188 -2.330793 0.0230318
L_AIP -0.0113568 -2.304410 0.0245627
L_FEF -0.0107215 -2.286783 0.0256352
R_33pr -0.0137511 -2.278372 0.0261615
L_SFL -0.0099751 -2.257008 0.0275413
L_PFcm -0.0136509 -2.195025 0.0319151
R_V2 -0.0063752 -2.191619 0.0321722
L_MBelt 0.0115537 2.511568 0.0146386

[[2]]

bias distractor
region m t_stat p
L_AIP -0.0212877 -4.674952 0.0000163
R_IP1 -0.0184820 -3.493357 0.0008860
R_AIP -0.0153805 -3.469555 0.0009542
L_23c -0.0139228 -3.260545 0.0018086
L_3a -0.0100848 -3.193939 0.0022067
L_31a -0.0213344 -3.126364 0.0026936
R_6d -0.0137850 -3.073493 0.0031429
L_ProS -0.0197267 -3.048726 0.0033766
L_8C -0.0132799 -3.011497 0.0037585
L_45 -0.0134355 -3.008660 0.0037892
L_43 -0.0131775 -3.004851 0.0038308
R_46 -0.0134912 -2.951793 0.0044559
L_IFJp -0.0127375 -2.943459 0.0045623
R_PIT -0.0188870 -2.938775 0.0046231
L_IFSa -0.0143782 -2.937558 0.0046391
L_3b -0.0101955 -2.925416 0.0048008
R_V3 -0.0107110 -2.794517 0.0069079
L_10r -0.0171208 -2.721465 0.0084257
L_2 -0.0098227 -2.679113 0.0094399
L_1 -0.0087764 -2.626309 0.0108603
L_SCEF -0.0098496 -2.597290 0.0117212
R_RI -0.0131179 -2.488106 0.0155440
L_IP1 -0.0130168 -2.473957 0.0161142
L_p47r -0.0136382 -2.447032 0.0172514
L_RI -0.0123859 -2.416361 0.0186343
L_AAIC -0.0197768 -2.385860 0.0201072
L_PHT -0.0114865 -2.341141 0.0224549
L_TPOJ1 -0.0115369 -2.284238 0.0257935
R_V4 -0.0092954 -2.242215 0.0285341
R_LIPd -0.0126245 -2.202577 0.0313514
L_OP1 -0.0126697 -2.199893 0.0315507
L_TF -0.0059109 -2.197012 0.0317659
L_IP2 -0.0097816 -2.195516 0.0318781
L_d32 0.0126380 2.207770 0.0309689
R_FOP2 0.0164284 2.360591 0.0214053

[[3]]

bias incongruency
region m t_stat p
L_8C -0.1043779 -8.316682 0.0e+00
L_p9-46v -0.1066311 -8.107307 0.0e+00
R_IP1 -0.0958843 -7.523548 0.0e+00
L_PF -0.0834004 -7.214125 0.0e+00
R_IP0 -0.1047275 -7.150769 0.0e+00
L_IP1 -0.1002203 -7.026108 0.0e+00
L_7m -0.0871959 -6.942213 0.0e+00
L_FOP4 -0.0771766 -6.915359 0.0e+00
L_AIP -0.0851012 -6.913247 0.0e+00
R_POS2 -0.0775834 -6.380421 0.0e+00
R_AIP -0.0766218 -6.361702 0.0e+00
L_8Av -0.0599460 -6.331417 0.0e+00
L_V3CD -0.0797816 -6.119921 1.0e-07
L_POS2 -0.0737083 -6.078117 1.0e-07
L_PGp -0.0875286 -6.062959 1.0e-07
R_PH -0.0645990 -6.016553 1.0e-07
L_45 -0.0634733 -6.002445 1.0e-07
R_6r -0.0664633 -5.996154 1.0e-07
L_IP0 -0.0844680 -5.908741 2.0e-07
L_LIPd -0.0827873 -5.802064 2.0e-07
L_PFm -0.0632621 -5.781269 3.0e-07
L_PHT -0.0633172 -5.770060 3.0e-07
L_IP2 -0.0893015 -5.641682 4.0e-07
L_i6-8 -0.0813786 -5.628684 5.0e-07
L_6a -0.0550228 -5.573231 6.0e-07
R_DVT -0.0659682 -5.468045 9.0e-07
L_PGs -0.0746622 -5.388997 1.2e-06
R_PGp -0.0782775 -5.341079 1.4e-06
L_IFJa -0.0804934 -5.337112 1.4e-06
L_8BL -0.0600722 -5.322898 1.5e-06
R_8Av -0.0502591 -5.315056 1.5e-06
L_IPS1 -0.0700892 -5.293920 1.7e-06
R_PFm -0.0547894 -5.290968 1.7e-06
R_V3CD -0.0768837 -5.246912 2.0e-06
L_IFSp -0.0821299 -5.230131 2.1e-06

4 Bar plots

## prep for plotting:

dat_barplots <- dat %>% 
  mutate(
    term = factor(term, levels = c("target", "incongruency", "distractor")),
    region_nice = gsub("17Networks_", "", region)
    ) %>%
  filter(term %in% c("target", "distractor", "incongruency"))

dat_barplots <- dat_barplots %>%
  pivot_wider(names_from = "session", values_from = "b") %>%
  mutate(b = proactive - baseline, session = "pro - bas") %>%
  select(-proactive, -baseline) %>%
  rbind(dat_barplots)

4.1 ROIs

atlas$data %>%
  ggplot() +
    geom_brain(
      aes(fill = ifelse(region %in% rois$region, region, NA)),
      atlas = atlas, position = position_brain(side ~ hemi)
      ) +
  labs(title = paste0("ROIs: ", roiset), fill = NULL) +
  theme_surface

for (ttypesub in ttype_subsets) {
  
  p <- dat_barplots %>%
    
    filter(ttype_subset == ttypesub) %>%
    right_join(rois, by = "region") %>%
    
    ggplot(aes(region_nice, b, fill = term, color = term)) +
    geom_hline(yintercept = 0, color = "grey70") +
    stat_summary(
      fun.data = mean_cl_boot, geom = "errorbar", width = 0, size = 2/3, position = position_dodge(width = 0.5),
      color = "grey50"
      ) +
    stat_summary(fun = mean, geom = "col", width = 1/3, position = position_dodge(width = 0.5)) +
    
    scale_color_brewer(type = "qual", palette = 2) +
    scale_fill_brewer(type = "qual", palette = 2) +
    
    facet_grid(cols = vars(session), rows = vars(location), scales = "free_y") +
    theme(
      #axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5), 
      legend.position = "none", axis.title.y = element_blank()
      ) +
      labs(y = "mean coefficient", title = paste0("ROIs: ", roiset)) +
    coord_flip() +
    labs(title = ttypesub)
  
  print(p)
  
}